Deriving Uncertain Knowledge in Knowledge Graphs Under Theoretical Mapping of Heuristics

Abstract

This paper proposes a deterministic, explainable framework for validating uncertain entities during Knowledge Graph (KG) expansion. The contribution is conceptual and formal: we present the framework and demonstrate its internal mathematical coherence, while empirical evaluation is explicitly deferred to future work. Since adding unverified nodes is trivial but removing structurally corrupted data afterwards is computationally hard, the multiphase Let's Build Knowledge (LBK) Framework acts as a gatekeeper that prevents such corruption at the point of entry. Drawing on cognitive heuristics, molecular data patterns, and meta-reasoning, LBK offers a deterministic alternative to prevalent stochastic and embedding-based approaches in Explainable AI (XAI): it integrates Formal Concept Analysis (FCA) with a localized adaptation of Byzantine Fault Tolerance (BFT) to scrutinize incoming structures, enabling loss-free, rule-based verification of new entities before global integration. We provide a precise specification of this hybrid model and demonstrate its formal capacity for deterministic uncertainty management, without claiming empirical validation.